Motion dependent video signal processing

ABSTRACT

In, for example, a motion compensated video standards converter wherein blocks in a first field or frame of a video signal are each compared with a plurality of blocks in the following field or frame of the video signal for deriving motion vectors representing the motion of the content of respective blocks between the first field or frame and the following field or frame, further motion vectors are selected for assignment to those of the blocks for which less than a predetermined plurality of good motion vectors are derived as a result of the comparison, by ranking all the good, non-stationary motion vectors derived for the field or frame in order of frequency of occurrence, and selecting those motion vectors which occur most frequently, unless they fall within a predetermined window of pixel motion of a motion vector already selected.

BACKGROUND OF THE INVENTION

1. Field of the Invention

Attention is drawn to the fact that this application is one of a seriesof fourteen filed on the same day, and bearing Ser. Nos. 07/511,739;07/511,740; 07/511,799; 07/512,145; 07/512,253; 07/512,262; 07/512,263;07/512,278; 07/512,279; 07/512,381; 07/512,813; 07/513,086; 07/513,087;and 07/513,426.

This invention relates to motion dependent video signal processing. Moreparticularly, the invention relates to video standards converters usingsuch signal processing, and to methods of deriving motion vectorsrepresenting motion between fields or frames of a video signal.

2. Description of the Prior Art

Video standards converters are well known devices used to convert videosignals from one standard to another, for example, from a 625 lines perframe, 50 fields per second standard to a 525 lines per frame, 60 fieldsper second standard. Video standards conversion cannot be achievedsatisfactorily merely by using simple linear interpolation techniques,because of the temporal and vertical alias which is present in a videosignal. Thus, simple linear interpolation produces unwanted artifacts inthe resulting picture, in particular, the pictures are blurredvertically and judder temporally.

To reduce these problems it has been proposed that video standardsconverters should use adaptive techniques to switch the parameters of alinear interpolator in dependence on the degree of movement in thepicture represented by the incoming video signal.

It has also been proposed, for example for the purpose of data reductionin video signal processing, to generate motion vectors from an incomingvideo signal by a block matching technique, in which the content of asearch block in one field or frame is compared with the respectivecontents of a plurality of search blocks comprised in a search area inthe following field or frame, to determine the minimum differencebetween the contents so compared, and hence the direction and distanceof motion (if any) of the content of the original search block.

The present invention is particularly concerned with the problem ofproviding motion vectors for those search blocks for which suchcomparison yields insufficient motion vectors.

SUMMARY OF THE INVENTION

One object of the present invention is to provide a motion compensatedvideo standards converter with improved means for selecting motionvectors.

Another object of the present invention is to provide a motioncompensated video standards converter with means for deriving motionvectors for search blocks for which block matching yields insufficientmotion vectors.

Another object of the present invention is to provide a method ofderiving motion vectors by comparison of blocks in fields or frames of avideo signal, and for allocating motion vectors to those blocks forwhich the initial derivation fails to yield sufficient motion vectors.

According to the present invention there if provided a motioncompensated video standards converter comprising:

means for comparing blocks in a first field or frame of a video signalmeans for comparing blocks in a first field or frame of a video signalwith a plurality of blocks in the following field or frame of the videosignal for deriving motion vectors representing the motion of thecontent of respective said blocks between said first field or frame andsaid following field or frame;

means to select further motion vectors for assignment to those of saidblocks for which less than a predetermined plurality of good motionvectors are derived as a result of said comparison, by ranking all thegood, non-stationary motion vectors derived for said field or frame inorder of frequency of occurrence, and selecting those motion vectorswhich occur most frequently, unless they fall within a predeterminedwindow of pixel motion of a motion vector already selected; and

an interpolator controlled in dependence on said motion vectors.

According to the present invention there is also provided a method ofderiving motion vectors representing motion between successive fields orframes of a video signal, the method including the steps of:

comparing blocks in a first field or frame of the video signal with aplurality of blocks in the following field or frame of the video signalfor deriving said motion vectors representing the motion of the contentof respective said blocks between said first or frame and said followingfield or frame; and

selecting a plurality of motion vectors for assignment to those of saidblocks for which less than a predetermined plurality of good motionvectors are derived as a result of said comparison, by ranking all thegood, non-stationary motion vectors derived for said field or frame inorder of frequency of occurrence, and selecting those motion vectorswhich occur most frequently, unless they fall within a predeterminedwindow of pixel motion of a motion vector already selected.

The above, and other objects, features and advantages of this inventionwill be apparent from the following detailed description of illustrativeembodiments which is to be read in connection with the accompanyingdrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an embodiment of motion compensated videostandards converter according to the present invention;

FIG. 2 shows diagrammatically progressive scan conversion;

FIGS. 3 to 6 show diagrammatically sequences of lines in sequences offields for explaining progressive scan conversion;

FIG. 7 is a block diagram showing the steps in motion adaptiveprogressive scan conversion;

FIG. 8 shows diagrammatically progressive scanning, in particular therequired estimate and difference value between successive fields;

FIGS. 9 and 10 are diagrams used in explaining the technique of FIG. 8in more detail, FIG. 9 showing a progressive scan normalizing functionand FIG. 10 showing a progressive scan non-linear function;

FIG. 11 shows diagrammatically the creation of pixels in missing linesin progressive scan conversion;

FIGS. 12 and 13 shown diagrammatically search blocks and search areas,and the relationships therebetween;

FIG. 14 shows a correlation surface;

FIGS. 15 and 16 show diagrammatically how a search block is grown;

FIG. 17 shows the areas of a frame in which search block matching is notpossible;

FIG. 18 shows diagrammatically a moving object straddling three searchblocks;

FIGS. 19 to 21 show three resulting correlation surfaces, respectively;

FIGS. 22 and 23 show further examples of correlation surfaces, used indescribing a threshold test;

FIGS. 24 and 25 show still further examples of correlation surfaces,used in describing a rings test;

FIG. 26 shows diagrammatically how the direction in which a search blockis to grow is determined;

FIG. 27 shows diagrammatically how a correlation surface is weighted;

FIG. 28 shows the relationship between sample blocks and search blocks,and a frame of video;

FIG. 29 shows part of the embodiment in more detailed block diagrammaticform;

FIG. 30 is a flow chart of the operation of the arrangement of FIG. 29;

FIG. 31 shows motion vector regions in a frame of video;

FIGS. 32 to 34 show diagrams used in explaining motion vector reductionin respective regions of a frame of video;

FIGS. 35 and 36 show diagrammatically a first stage in motion vectorselection;

FIGS. 37 and 38 show diagrammatically how a threshold is establishedduring the motion vector selection;

FIG. 39 shows diagrammatically a second stage in motion vectorselection;

FIGS. 40 to 46 show arrays of pixels with associated motion vectors,used in explaining motion vector post-processing; and

FIG. 47 shows diagrammatically the operation of an interpolator.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

The embodiment of motion compensated video standards converter to bedescribed is particularly intended for use in the conversion of a highdefinition video signal (HDVS) having 1125 lines per frame, 60 fieldsper second, to 24 frames per second 35 mm film. However, it will beunderstood that the invention is not limited in this respect, and thatthe standards converter can readily be adapted to effect conversionsbetween other standards.

FIG. 1 is a block diagram of the standards converter. The standardsconverter comprises an input terminal 1 to which an input video signalis supplied. The input terminal is connected to a progressive scanconverter 2 in which the input video fields are converted into videoframes which are supplied to a direct block matcher 3 whereincorrelation surfaces are created. These correlation surfaces areanalysed by a motion vector estimator 4, which derives and suppliesmotion vectors to a motion vector reducer 5, wherein the number ofmotion vectors for each pixel is reduced, before they are supplied to amotion vector selector 6, which also receives an output from theprogressive scan converter 2. Any irregularity in the selection of themotion vector by the motion vector selector 6 is removed by a motionvector post processor 7, from which the processed motion vectors aresupplied to and control an interpolator 8 which also receives an inputfrom the progressive scan converter 2. The output of the interpolator 8,which is a standards-converted and motion-compensated video signal issupplied to an output terminal 9. Each part of the standards converterand the operation thereof will be described in more detail below.

The progressive scan converter 2 produces output frames at the same rateas the input fields. Thus, referring to FIG. 2 which shows a sequence ofconsecutive lines in a sequence of consecutive fields, the crossesrepresenting lines present in the input fields and the squaresrepresenting interpolated lines, each output frame will contain twicethe number of lines as an input field, the lines alternating betweenlines from the input video signal and lines which have been interpolatedby one of the methods to be described below. The interpolated lines canbe regarded as an interpolated field of the opposite polarity to theinput field, but in the same temporal position.

Progressive scan conversion is preferably carried out, for two mainreasons; firstly, to make the following direct block matching processeasier, and secondly in consideration of the final output video format.These two reasons will now be considered in more detail.

Direct block matching is used to obtain an accurate estimation of thehorizontal and vertical motion between two successive video fields, asdescribed in more detail below. However, due to the interlaced structureof the video signal on which direct block matching is performed,problems can arise.

Consider the image represented by FIG. 3, which indicates a sequence ofsuccessive lines in a sequence of successive fields, the open squaresrepresenting white pixels, the black squares representing black pixels,and the hatched squares representing grey pixels. This, therefore,represents a static picture with a high vertical frequency componentwhich in a HDVS would be 1125/3 cycles per picture height. As this imagehas been sampled by the usual interlace scanning procedure, each fieldappears to contain a static vertical frequency luminance component Y of1125/6 cph, as indicated in FIG. 4. However, the frequency components ineach field are seen to be in anti-phase. Attempts to perform directblock matching between these two fields will lead to a number ofdifferent values for the vertical motion component, all of which areincorrect. This is indicated in FIG. 5, in which the abbreviation LPFmeans lines per field. From FIG. 5 it is clear that direct blockmatching will not give the correct answer for the vertical motioncomponent, which component should in fact be zero. This is because thedirect block matching is in fact tracking the alias component of thevideo signal rather than the actual motion.

Consider now FIG. 6, which depicts the same static image as FIG. 3,except that now each input field has been progressive scan converted toform a frame, the triangles representing interpolated pixels. It can beseen that each frame now contains the same static vertical frequencycomponent as the original input fields, that is 1125/3 cph. Thus, directblock matching between two successive frames can now give the correctvalue for the vertical motion, that is, zero, and the tracking of thevertical alias has been avoided. Moreover, there is the point thatdirect block matching on progressive scan converted frames will resultin a more accurate vertical motion estimate, because the direct blockmatching is being performed on frames which have twice the number oflines.

Concerning consideration of the final output video format, in the caseof the present embodiment, the converted video is supplied via tape tobe electron beam recorder, and needs to consist of frames correspondingto the motion picture film rate of 24 frames per second. For thisreason, therefore, the production of progressive scan converted framesis necessary, and moreover the progressive scan converted frames canalso be used as a fall-back in the case where motion compensatedstandards conversion is deemed to be producing unacceptable results, forexample, where the motion is too diverse to be analysed satisfactorily.In that case the use of the nearest progressive scan converted frame asthe required output frame can produce reasonably acceptable results.

Progressive scan conversion can be carried out in a number of ways, suchas by previous field replacement, median filtering in which threespatially consecutive lines are examined (temporally these three lineswill come from two consecutive fields), or a motion compensatedtechnique which utilizes multi-gradient motion detection followed bymulti-direction linear interpolation. However, in the present embodimentthe preferred method is motion adaptive progressive scan conversion, thesteps of which are indicated in the block diagram of FIG. 7. The conceptis to use inter-field interpolation in wholly static picture areas toretain as much vertical information as possible, and to use intra-fieldinterpolation when significant motion is present. This also aids smoothportrayal of motion. In scenes where the motion is somewhere betweenthese two extremes, as estimate of the local motion present in thepicture is made, and this is then used to mix together differentproportions of inter- and intra-field interpolation.

In more detail, the modulus of the frame difference between previous andnext fields generated, this being indicated in FIG. 8. To generate therequired estimates, the modulus inter-frame difference array from theprevious and the next fields is generated at each point: ##EQU1## where:Δ_(U) is the unnormalized modulus difference array, and

Y is the luminance array corresponding to the 3D picture.

The modulus of difference is then normalized to adjust for thesignificance of changes in lower luminance areas; ##EQU2## where: Δ_(N)is the normalized modulus difference array

Y is the inter-frame average luminance value ##EQU3## F(Y) (thenormalizing function) is derived as indicated in FIG. 9.

The difference array ΔN is then vertically filtered together with theprevious field difference by a three-tap filter (examples ofcoefficients are a quarter, a half, a quarter or zero, unity, zero) toreduce vertical alias problems, and in particular to minimize theproblems encountered with temporal alias. Thus: ##EQU4## where: Δ_(F) isthe filtered normalized difference array, and

C₁ and C₂ are filter coefficients, and 2C₁ +C₂ =1 so that unity dc gainis maintained.

A vertical and horizontal intra-field filter of up to five taps byfifteen taps are then used to smooth the difference values within thecurrent field. In practices, a filter of three taps by three taps issatisfactory. Finally, in order to produce the actual motion estimation,a non-linear mapping function is applied using a function to provide themotion estimate (ME):

    ME (pixel, current line)=γ(spatially filtered Δ.sub.F (pixel, current line))

The non-linear function γ derived as shown in FIG. 10, the staticpicture ME is zero, for full motion ME is one, and for intermediatemotions a controlled transition occurs.

To produce an interpolated pixel, the pixels in the missing line arecreated by taking proportions of the surrounding lines as indicated inFIG. 11. The motion estimate ME is then applied to the intra-frameinterpolated value (generated from a two, four, six or preferably eighttap filter), and 1-ME is applied to the inter-field average (oralternatively to a more complex interpolated value), and these aresummed to derive the progressive scan pixel estimate; ##EQU5## where:C₀, C₁, C₂ and C₃ are the intra-frame filter coefficients, and 2(C₀ +C₁+C₂ +C₃)=1 so that unity dc gain is maintained.

This method of progressive scan conversion is found to produce highquality frames from input fields, in particular because a moving objectcan be isolated and interpolated in a different manner to a stationarybackground.

Referring back to FIG. 1, the frames of video derived by the progressivescan converter 2 are used to derive motion vectors. The estimation ofmotion vectors consists of two steps. Firstly, correlation surfaces aregenerated by correlating search blocks from consecutive frames. Then,having obtained these correlation surfaces, they have to be examined todetermine the position or positions at which correlation is best.Several different methods of obtaining a correlation surface exist, thetwo main methods being phase correlation and direct block matching.There are, however, a number of problems associated with the use ofphase correlation, these being very briefly problems relating to thetransform mechanism, the windowing function, the block size and thevariable quality of the contour of the surface produced. In the presentembodiment, therefore, direct block matching is preferred.

The direct block matcher 3 operates as follows. Two blocks, respectivelycomprising a rectangular array of pixels from consecutive frames of theprogressive scan converted video signal are correlated to produce acorrelation surface from which a motion vector is derived.

Referring to FIG. 12, firstly a small block called a search block ofsize 32 pixels by 23 lines is taken from a frame as shown in FIG. 12.Then a larger block called a search area of size 128 pixels by 69 linesis taken from the next frame. The search block (SB) is then placed ineach possible position in the search area (SA) as shown in FIG. 13, andfor each location the sum of the absolute difference of pixel luminancelevels between the two blocks is calculated. This value is then used asthe height of the correlation surface at the point at which it wasderived. It can then be used in conjunction with other similarly derivedvalues for each possible location of the search block in the search areato obtain a correlation surface, an example of which is shown in FIG.14. For clarity the surface is shown inverted, and as it is in fact theminimum that is required, the required point in FIG. 14 is the mainpeak.

The size of the search block is selected by examining the minimum sizeof an object that may require motion compensation. For PAL 625 lines perframe, 50 fields per second signals a search block of 16 pixels by 8lines has been found suitable for tracking a small object withoutallowing any surrounding information not within the object, but stillwithin the search block, to affect the tracking of the object. Thisapproach has therefore been adopted in the present embodiment, butmodified to take account of the different numbers of active pixels perline, active lines per frame, and aspect ratio of a HDVS as comparedwith PAL 625/50. The comparative figures, the HDVS being put first, areas follows; 1920 (720) active pixels per line, 1035 (575) active linesper frame, 3:5.33 (3:4) aspect ratio.

It should be added that there is an argument for using a larger searchblock, since this means that a large object can be tracked. On the otherhand, there exists an argument for using a smaller search block, toprevent a small object being over-shadowed by the effect of a largeobject or background area. Also, however, there is the advantage thatwith small search blocks there is no requirement for the derivation ofmore than one motion vector from each of them. Because having a singlemotion vector is so much easier than having more than one, the presentembodiment starts with a small search block as described above, and thencauses the search block to grow into a bigger search block if nosatisfactory result has been obtained. This then encompasses theadvantages of both a small and a large search block. The criteria for asatisfactory result is set by the motion vector estimator 4 (FIG. 1)referred to in more detail below and which determines the motion vectorfrom a given correlation surface.

This technique of causing the search block to grow is not onlyadvantageous for tracking large objects. It can also help to track themovement of an object having the shape of a regular pattern of aperiodic nature. Thus, consider FIG. 15 where a search block A willmatch up with the search area B at locations V1, V2 and V3, with each ofthem giving a seemingly correct measure of motion. In this case,however, the motion vector estimation, that is the process that actuallyanalyses the correlation surface, will show that good correlation occursin three locations which are collinear. The search block will thereforebe caused to grow horizontally until it is three times its originalwidth, this being the direction in which multiple correlation occurredin this case. The search area will also be correspondingly horizontallyenlarged. As shown in FIG. 16, with the enlarged search block 3A, thereis only a single correlation point, which correctly relates to themotion of the object.

In this particular case the search block and search area both have togrow horizontally, because the direction of multiple correlation ishorizontal. It is equally possible, however, for the search block andthe search area to grow vertically, or indeed in both directions, if thecorrelation surface suggests it.

It should be noted that block matching cannot be applied to all thesearch blocks in the frame, because in the border area there is notenough room from which a search area can be drawn. Thus, block matchingcannot be effected in the border area of the frame shown hatched in FIG.17. This problem is dealt with by the motion vector reducer 5 (FIG. 1)described in more detail below, which attempts to supply search blocksin this hatched area with appropriate motion vectors.

From the correlation surface (FIG. 14) generated for each search blockin a frame the motion vector estimator 4 (FIG. 1) deduces the likelyinter-frame motion between the search block and its corresponding searcharea. It should again be mentioned that for clarity all diagrams ofcorrelation surfaces are shown inverted, that is, such that a minimum isshown as a peak.

The motion vector estimator 4 (FIG. 1) uses motion vector estimationalgorithms to detect the minimum point on each correlation surface. Thisrepresents the point of maximum correlation between the search block andthe search area, and hence indicates the probable motion between them.The displacement of this minimum on the correlation surface with respectto the origin, in this case the centre of the surface, is a directmeasurement, in terms of pixels per frame, of the motion. For thesimplest case, where the correlation surface contains a single, distinctminimum, the detection of the minimum point on the correlation surfaceis sufficient to determine accurately the motion between the searchblock and the search area. As previously mentioned, the use of smallsearch blocks improves the detection of motion and the accuracy ofmotion estimation, but unfortunately small single search blocks areunable to detect motion in a number of circumstances which will now bedescribed.

FIG. 18 shows an object with motion vectors (5, 0) straddling threesearch blocks 1A, 2A and 3A in a frame (t). When the search blocks 1Aand 3A are correlated with respective search areas (1B and 3B) in thenext frame (t+1) a correlation surface shown in FIG. 19 results showinga minimum at (5, 0). (This assumes a noiseless video source.) However,when the search block 2A is correlated with its respective search area2B, the correlation surface shown in FIG. 20 is produced, in which thesearch block 2A correlates with the search area 2B at every point in they-axis direction. There is therefore no single minimum in thecorrelation surface, and hence the motion between the search block 2Aand the search area 2B cannot be determined.

However, now consider the situation if the search block 2A is grown suchthat it encompasses all three of the original search blocks 1A, 2A and3A. When the grown search block 2A is correlated with a search areacovering the original search areas 1B, 2B and 3B, the resultingcorrelation surface is as shown in FIG. 21. This shows a single minimum(5, 0) indicating the correct motion of the original search block 2A.This example illustrates the need for some unique feature in the sourcevideo, in order accurately to detect motion. Thus, the search blocks 1Aand 3A both had unique vertical and horizontal features, that is theedges of the object, and hence motion could be determined. In contrast,the search block 2A had a unique vertical feature, but no uniquehorizontal feature, and hence horizontal motion could not be determined.However, by growing the search block until it encompasses a uniquefeature both horizontally and vertically, the complete motion for thatsearch block can be determined. Moreover, it can be shown that growingthe search block is beneficial when noise in the source video isconsidered.

A further example will now be considered with reference to FIG. 22. Thisshows a correlation surface for a search block where the motion vectoris (5, 3). However, due to the numerous other correlations which havetaken place between the search block and the search area, the truemotion is difficult to detect. An example of source video which mightproduce such a correlation surface would be a low contrast tree movingwith the wind. It is now assumed that the search block and the searcharea are grown. The growing can take place in the horizontal direction,as in the previous example, or in the vertical direction, or in bothdirections. Assuming that the neighbouring search blocks have the samemotion, the mean effect on the resulting correlation surface will be toincrease the magnitude of the minima at (5, 3) by a greater proportionthan the magnitude of the other correlation peaks. This is shown in FIG.23, which indicates that it is then easier to detect the correct motionvector.

The way in which search blocks are grown will now be further consideredwith reference to FIG. 18. Here it was required to grow the area of thesearch block 2A to encompass the areas of the search blocks 1A and 3A,and to produce the resulting correlation surface. In fact, the resultingcorrelation surfaces are produced directly by adding together theelements of the three correlation surfaces corresponding to the searchblocks 1A, 2A and 3A. In effect, if each correlation surface isconsidered as a matrix of point magnitudes, then the correlation surfaceof the enlarged search block 2A is the matrix addition of thecorrelation surface of the original search blocks 1A, 2A and 3A.

The area of the search block 2A could also be grown vertically by addingcorrelation surfaces of the search blocks above and below, whilst if thesearch block 2A is to be grown both horizontally and vertically, thenthe four neighbouring diagonal correlation surfaces have to be added aswell. From this it will be seen that the actual process of growing asearch blocks to encompass neighbouring search blocks is relativelyeasy, the more difficult process being to decide when growing shouldtake place, and which neighbouring search blocks should be encompassed.Basically, the answer is that the area of the search blocks should begrown until a good minimum or good motion vector is detected. It istherefore necessary to specify when a motion vector can be taken to be agood motion vector, and this can in fact be deduced from the examplesgiven above.

In the example described with reference to FIGS. 18 to 21, it wasnecessary to grow the search block horizontally in order to encompass aunique horizontal feature of the object, and hence obtain a singleminimum. This situation was characterized by a row of identical minimaon the correlation surface of FIG. 20, and a single minimum on thecorrelation surface of FIG. 21. From this the first criteria for a goodminimum can be obtained; a good minimum is the point of smallestmagnitude on the correlation surface for which the difference between itand the magnitude of the next smallest point exceeds a given value. Thisgiven value is known as the threshold value, and hence this test isreferred to herein as the threshold test.

It should be noted that the next smallest point is prevented fromoriginating from within the bounds of a further test, described below,and referred to herein as the rings test. In the case of a rings testemploying three rings, the next smallest point is prevented fromoriginating from a point within three pixels of the point in question.In the example of FIGS. 18 to 21, the correlation surface of FIG. 20would have failed the threshold test; the search area 2A is thereforegrown and, given a suitable threshold value, the correlation surface ofFIG. 21 will pass the threshold test.

The threshold test can also be used to cause growing in the exampledescribed above with reference to FIGS. 22 and 23. Prior to growing thesearch block, the correct minimum is undetectable, due to the closelysimilar magnitudes of the surrounding points. Given a suitable thresholdvalue, however, the correlation surface will fail the threshold test,and the search block will be grown. As a result, it will then bepossible to detect the minimum among the other spurious points.

It will be seen that the use of a threshold is a subjective test, butthe correct threshold for the correlation surface under test can beselected by normalizing the threshold as a fraction of the range ofmagnitudes within the correlation surface. This also lessens the effectof, for example the contrast of the video source.

The rings test, referred to briefly above, and which is far lesssubjective, will now be further described. The basis of the rings testis to assume that a good minimum (or maximum) will have points ofincreasing (or decreasing) magnitudes surrounding it. FIG. 24illustrates this assumption, showing a minimum at (0, 0) where thesurrounding three rings of points have decreasing mean magnitude. Thisis as opposed to the correlation surface shown in FIG. 25, where therings, and in particular the second inner-most ring, are not ofdecreasing mean magnitude.

In this case the criteria for a good minimum as defined by the ringstest, is that the average slope is monotonic. Therefore for apre-defined number of rings of points surrounding the minimum inquestion, the mean magnitude of each ring when moving from the innermostring outwards, must be greater than that of the previous ring. Returningagain to the example described with reference to FIGS. 18 to 21, it willbe seen from FIGS. 20 and 21 that the correlation surface of FIG. 20would have failed the rings test, but that the correlation surface ofFIG. 21 would have passed the rings test. Since the rings test comparesmean, and not absolute, magnitudes, it is far less subjective than thethreshold test, and indeed the only variable in the rings test is thenumber of rings considered.

Having described the mechanism for growing a search block, it is nownecessary to consider how by examining the shape of the correlationsurface it is possible to determine the most effective direction inwhich the search block should grow.

Referring again to FIG. 20, this correlation surface resulted wherethere was a unique vertical feature, but no unique horizontal feature.This is mirrored in the correlation surface by the minimum runninghorizontally across the correlation surface, due to the multiplecorrelations in this direction. From this it can be deduced that thesearch block should be grown horizontally. Conversely, should a line ofmultiple correlations run vertically, this would indicate the need togrow the search block vertically, whilst a circular collection ofmultiple correlations would indicate a need to grow the search blockboth horizontally and vertically.

Using this criteria, a quantitative measure of the shape of thecorrelation surface is required in order to determine in which directionthe search block should be grown. This measure is determined as follows.Firstly, a threshold is determined. Any point on the correlation surfacebelow the threshold is then considered. This threshold, like that usedin the threshold test, is normalized as a fraction of the range ofmagnitudes within the correlation surface. Using this threshold, thepoints on the correlation surface are examined in turn in four specificsequences. In each, the point at which the correlation surface valuefalls below the threshold is noted. These four sequences are illustrateddiagrammatically in FIG. 26 in which the numbers 1, 2, 3 and 4 at thetop bottom, left and right refer to the four sequences, and the hatchedarea indicates points which fall below the threshold;

Sequence 1

Search from the top of the correlation surface down for a point A whichfalls below the threshold.

Sequence 2

Search from the bottom of the correlation surface up for a point C whichfalls below the threshold.

Sequence 3

Search from the left of the correlation surface to the right for a pointD which falls below the threshold.

Sequence 4

Search from the right of the correlation surface to the left for a pointB which falls below the threshold.

The locations of the four resulting points A, B, C and D are used tocalculate the two dimensions X and Y indicated in FIG. 26, thesedimensions X and Y indicating the size of the hatched area containingthe points falling below the threshold value. Hence from the dimensionsX and Y, it can be deduced whether the shape is longer in the x ratherthan the y direction, or vice versa, or whether the shape isapproximately circular. A marginal difference of say ten percent isallowed in deducing the shape, that is, the dimension X must be aminimum of ten percent greater than the dimension Y for the shape to beconsidered to be longer in the x direction. Similarly for the ydirection. If the dimensions X and Y are within ten percent of eachother, then the shape is considered to be circular, and the search blockis grown in both directions. In the example of FIG. 26 the dimension Xis greater than the dimension Y, and hence the search block is grown inthe x or horizontal direction.

The growing of the search block continues until one or more growthlimitations is reached. These limitations are: that the minimum in thecorrelation surface passes both the threshold test and the rings test;that the edge of the video frame is reached; or that the search blockhas already been grown a predetermined number of times horizontally andvertically. This last limitation is hardware dependent. That is to say,it is limited by the amount of processing that can be done in theavailable time. In one specific embodiment of apparatus according to thepresent invention, this limit was set at twice horizontally and oncevertically.

If the minimum in the correlation surface passes both the threshold testand the rings test, then it is assumed that a good motion vector hasbeen determined, and can be passed to the motion vector reducer 5 (FIG.1). However, if the edge of the frame is reached or the search block hasalready been grown a predetermined number of times both horizontally andvertically, then it is assumed that a good motion vector has not beendetermined for that particular search block, and instead of attemptingto determine a good motion vector, the best available motion vector isdetermined by weighting.

The correlation surface is weighted such that the selection of the bestavailable motion vector is weighted towards the stationary, that is thecentre, motion vector. This is for two reasons, firstly, if the searchblock, even after growing, is part of a large plain area of sourcevideo, it will not be possible to detect a good motion vector. However,since the source video is of a plain area, a stationary motion vectorwill lead to the correct results in the subsequent processing. Secondly,weighting is designed to reduce the possibility of a seriously wrongmotion vector being passed to the motion vector reducer 5 (FIG. 1). Thisis done because it is assumed that when a good motion vector cannot bedetermined, a small incorrect motion vector is preferable to a largeincorrect motion vector.

FIG. 27 shows an example of how the weighting function can be applied tothe correlation surface. In this example, the weight applied to a givenpoint on the correlation surface is directly proportional to thedistance of that point from the stationary, centre motion vector. Themagnitude of the point on the correlation surface is multiplied by theweighting factor. For example, the gradient of the weighting functionmay be such that points plus or minus 32 pixels from the centre,stationary motion vector are multiplied by a factor of three. In otherwords, as shown in FIG. 27, where the centre stationary motion vector isindicated by the black circle, the weighting function is an invertedcone which is centred on the centre, stationary motion vector.

After the correlation surface has been weighted, it is again passedthrough the threshold test and the rings test. If a minimum which passesboth these tests is determined, then it is assumed that this is a goodmotion vector, and it is flagged to indicate that it is a good motionvector, but that weighting was used. This flag is passed, together withthe motion vector to the motion vector reducer 5 (FIG. 1). If on theother hand, neither a good motion vector nor a best available motionvector can be determined, even after weighting, then a flag is set toindicate that any motion vector passed to the motion vector reducer 5(FIG. 1) for this search block is a bad motion vector. It is necessaryto do this because bad motion vectors must not be used in the motionvector reduction process, but must be substituted as will be describedbelow.

Thus, in summary, the operation of the motion vector estimator 4(FIG. 1) is to derive from the correlation surface generated by thedirect block matcher 3 (FIG. 1), the point of best correlation, that isthe minimum. This minimum is then subjected to the threshold test andthe rings test, both of which the minimum must pass in order for it tobe considered to represent the motion of the search block. It should,incidentally, be noted that the threshold used in the threshold test andthe rings test may be either absolute values or fractional values. Ifthe minimum fails either test, then the search block is grown, a newminimum id determined, and the threshold test and the rings testre-applied. The most effective direction in which to grow the searchblock is determined from the shape of the correlation surface.

Referring initially to FIG. 1, the process of motion vector reductionwill now be described. Using a HDVS, each search block is assumed to be32 pixels by 23 lines, which can be shown to lead to a possible maximumof 2451 motion vectors. The choice of the search block size is acompromise between maintaining resolution and avoiding an excessiveamount of hardware. If all these motion vectors were passed to themotion vector selector 6, the task of motion vector selection would notbe practicable, due to the amount of processing that would be required.To overcome this problem, the motion vector reducer 5 is providedbetween the motion vector estimator 4 and the motion vector selector 6.The motion vector reducer 5 takes the motion vectors that have beengenerated by the motion vector estimator 4 and presents the motionvector selector 6 with only, for example, four motion vectors for eachsearch block in the frame, including those in border regions, ratherthan all the motion vectors derived for that frame. The effect of thisis two-fold. Firstly, this makes it much easier to choose the correctmotion vector, so long as it is within the group of four motion vectorspassed to the motion vector selector 6. Secondly, however, it also meansthat if the correct motion vector is not passed as one of the four, thenthe motion vector selector 6 is not able to select the correct one. Itis therefore necessary to try to ensure that the motion vector reducer 5includes the correct motion vector amongst those passed to the motionvector selector 6. It should also be mentioned that although four motionvectors are passed by the motion vector reducer 5 to the motion vectorselector 6, only three of these actually represent motion, the fourthmotion vector always being the stationary motion vector which isincluded to ensure that the motion vector selector 6 is not forced intoapplying a motion vector representing motion to a stationary pixel.Other numbers of motion vectors can be passed to the motion vectorselector 6, for example, in an alternative embodiment four motionvectors representing motion and the stationary motion vector may bepassed.

Hereinafter the term `sample block` refers to a block to a frame ofvideo in which each pixel is offered the same four motion vectors by themotion vector reducer 5. Thus, a sample block is the same as a searchblock before the search block has been grown. As shown in FIG. 28, in aframe of video the initial positions of the sample blocks and the searchblocks are the same.

The motion vector reducer 5 (FIG. 1) receives the motion vectors and theflags from the motion vector estimator 4 (FIG. 1) and determines thequality of the motion vectors by examining the flags. If the motionvector was not derived from an ambiguous surface, that is there is ahigh degree of confidence in it, then it is termed a good motion vector,but if a certain amount of ambiguity exists, then the motion vector istermed a bad motion vector. In the motion vector reduction process, allmotion vectors classed as bad motion vectors are ignored, because it isimportant that no incorrect motion vectors are ever passed to the motionvector selector 6 (FIG. 1), in case a bad motion vector is selectedthereby. Such selection would generally result in a spurious dot in thefinal picture, which would by highly visible.

Each of the motion vectors supplied to the motion vector reducer 5(FIG. 1) was obtained from a particular search block, and hence aparticular sample block (FIG. 28), the position of these being notedtogether with the motion vector. Because any motion vectors which havebeen classed as bad motion vectors are ignored, not all sample blockswill have a motion vector derived from the search block at thatposition. The motion vectors which have been classed as good motionvectors, and which relate to a particular search block, and hence aparticular sample block, are called local motion vectors, because theyhave been derived in the area from which the sample block was obtained.In addition to this, another motion vector reduction process counts thefrequency at which each good motion vector occurs, with no account takenof the actual positions of the search blocks that were used to derivethem. These motion vectors are then ranked in order of decreasingfrequency, and are called common motion vectors. In the worst case onlythree common motion vectors are available and these are combined withthe stationary motion vector to make up the four motion vectors to bepassed to the motion vector selector 6 (FIG. 1). However, as there areoften more than three common motion vectors, the number has to bereduced to form a reduced set of common motion vectors referred to asglobal motion vectors.

A simple way of reducing the number of common motion vectors is to usethe three most frequent common motion vectors and disregard theremainder. However, the three most frequent common motion vectors areoften those three motion vectors which were initially within plus orminus one pixel motion of each other vertically and/or horizontally. Inother words, these common motion vectors were all tracking the samemotion with slight differences between them, and the other common motionvectors, which would have been disregarded, were actually trackingdifferent motions.

In order to select the common motion vectors which represent all or mostof the motion in a scene, it is necessary to avoid choosing globalmotion vectors which represent the same motion. Thus, the strategyactually adopted is first to take the three most frequently occurringcommon motion vectors and check to see if the least frequent among themis within plus or minus one pixel motion vertically and/or plus or minusone pixel motion horizontally of either of the other two common motionvectors. If it is, then it is rejected, and the next most frequentlyoccurring common motion vector is chosen to replace it. This process iscontinued for all of the most frequently occurring common motion vectorsuntil there are either three common motion vectors which are not similarto each other, or until there are three or less common motion vectorsleft. However, if there are more than three common motion vectors left,then the process is repeated this time checking to see if the leastfrequent among them is within plus or minus two pixel motion verticallyand/or plus or minus two pixel motion horizontally of another, and so onat increasing distances if necessary. These three common motion vectorsare the required global motion vectors, and it is important to not thatthey are still ranked in order of frequency.

This selection of the global motion vectors, with which the presentinvention is particularly, but not exclusively, concerned, will now bedescribed in more detail with reference to FIGS. 29 and 30.

FIG. 29 shows a circuit arrangement for effecting the requiredselection. The arrangement comprises a count system 20 includingcounters 21 to 24, buffers 25, 26, 28, 29, 32 and 33, a microprocessor27, a random access memory (RAM) 30, and a read only memory (ROM) 31which holds the program for the microprocessor 27, these elements beinginterconnected as shown and operating as will now be described.

The count system 20 is reset at the start of a frame and then the motionvectors for that frame are loaded by way of the buffer 32. While themotion vectors are being loaded, the microprocessor 27 is disabled, asit is also while the motion vectors are subsequently being read out byway of the buffer 33.

While the motion vectors are being loaded, the counter 21 counts theinput motion vectors, and hence controls the input of the motion vectorsto the RAM 30 by providing sample block addresses by way of the buffer25. Meanwhile, the counter 23 counts the sample blocks in the horizontaldirection of the frame, and the counter 24 counts the sample blocks inthe vertical direction of the frame. On completion of the frame, thecounter 24 causes the counter 22 to control the output of the motionvectors from the RAM 30 by providing sample block addresses by way ofthe buffer 26.

The RAM 30 has three areas. A first area is set aside to store theoriginal motion vectors. This area will only be read by themicroprocessor 27, and will never be written in by the microprocessor27. A second area is set aside to store the output motion vectors, asthis makes reading out easier. A third area is set aside for processing.

Initially then the microprocessor 27 and the motion vector outputsection are disabled while the incoming motion vectors are written inthe RAM 30. The motion vector input and output sections are thendisabled while processing according to the algorithm described below isperformed. Then the motion vector input section and the microprocessor27 are disabled while the motion vectors are read out of the RAM 30 bythe motion vector output section by way of the buffer 33.

In operation, the frequency of occurrence of each good motion vector,for the entire frame, is first calculated. If there are more than threenon-zero, that is non-stationary, motion vectors flagged as good, thenthese have to be reduced to three. However, for the reasons describedabove, the three most frequently occurring motion vectors are notnecessarily the three which best represent the motion in the entirescene.

Referring also to the flow chart of FIG. 30, if there are more thanthree good non-stationary motion vectors, then the second most frequentis compared in pixel motion with the most frequent. If they are bothwithin a predetermined window, for example if they are within ± onepixel motion of each other horizontally and/or vertically (some otherdistance can be selected), then the less frequent motion vector isdeleted from the list. The algorithm proceeds to the third most frequentmotion vector, and so on down the list until there are only three motionvectors left or the three most frequent motion vectors are not any ofthem within the predetermined window of another.

When considering the motion vector reduction process and the sampleblocks of a frame of video, it is necessary to look at three differenttypes of sample blocks. These types are related to their actual positionin a frame of video, and are shown in FIG. 31 as regions. Region Acomprises sample blocks which are totally surrounded by other sampleblocks and are not near the picture boundary. Region B contains sampleblocks which are partially surrounded by other sample blocks and are notnear the picture boundary. Finally, region C contains sample blockswhich are near the picture boundary. The motion vector reductionalgorithm to be used for each of these regions is different. Thesealgorithms will be described below, but firstly it should be reiteratedthat there exist good motion vectors for some of the sample blocks inthe frame of video, and additionally there are also three global motionvectors which should represent most of the predominant motion in thescene. A selection of these motion vectors is used to pass on threemotion vectors together with the stationary motion vector for eachsample block.

FIG. 32 illustrates diagrammatically motion vector reduction in theregion A. This is the most complex region to deal with, because it hasthe largest number of motion vectors to check. FIG. 32 shows a centralsample block which is hatched, surrounded by other sample blocks a to h.Firstly, the locally derived motion vector is examined to see if it wasclassed as a good motion vector. If it was, and it is also not the sameas the stationary motion vector, then it is passed on. However, if itfails either of these tests, it is ignored. Then the motion vectorassociated with the sample block d is checked to see if it was classedas a good motion vector. It is was, and if it is neither the same as anymotion vector already selected, nor the same as the stationary motionvector, then it too is passed on. If it fails any of these tests then ittoo is ignored. This process then continues in a similar manner in theorder e, b, g, a, h, c and f. As soon as three motion vectors, notincluding the stationary motion vector, have been obtained, then thealgorithm stops, because that is all that is required for motion vectorselection for that sample block. It is, however, possible for all theabove checks to be carried out without three good motion vectors havingbeen obtained. If this is the case, then the remaining spaces are filledwith the global motion vectors, with priority being given to the movefrequent global motion vectors.

FIG. 33 illustrates motion vector reduction in the region B. Sampleblocks in the region B are the same as those in the region A, exceptthat they are not totally surrounded by other sample blocks. Thus theprocess applied to these sample blocks is exactly the same as those forthe region A, except that it is not possible to search in all thesurrounding sample blocks. Thus as seen in FIG. 33, it is only possibleto check the motion vectors for the sample blocks a to e, and anyremaining spaces for motion vectors are filled, as before, with globalmotion vectors. Likewise, if the hatched sample block in FIG. 33 weredisplaced two positions to the left, then it will be seen that therewould only be three adjacent surrounding blocks to be checked beforeresorting to global motion vectors.

FIG. 34 illustrated motion vector reduction in the region C. This is themost severe case, because the sample blocks neither have a locallyderived motion vector nor do they have many surrounding sample blockswhose motion vectors could be used. The simplest way of dealing withthis problem is simply to give the sample blocks in the region C theglobal motion vectors together with the stationary motion vector.However, this is found to produce a block-like effect in the resultingpicture, due to the sudden change in the motion vectors presented forthe sample blocks in the region C compared with adjoining sample blocksin the region B. Therefore a preferred strategy is to use for the sameblocks in the region C the sample motion vectors as those used forsample blocks in the region B, as this prevents sudden changes.Preferably, each sample block in the region C is assigned the samemotion vectors as that sample block in the region B which is physicallynearest to it. Thus, in the example of FIG. 34, each of the hatchedsample blocks in the region C would be assigned the same motion vectorsas the sample block a in the region B, and this has been found to giveexcellent results.

Referring again to FIG. 1, the purpose of the motion vector selector 6is to assign one of the four motion vectors supplied thereto to eachindividual pixel within the sample block. In this way the motion vectorscan be correctly mapped to the outline of objects. The way in which thisassignment is effected is particularly intended to avoid the possibilityof the background surrounding fine detail from producing a better matchthan that produced by the correct motion vector. To achieve this themotion vector selection process is split into two main stages. In thefirst stage, motion vectors are produced for each pixel in the inputframes. In another words, there is no attempt to determine the motionvector values for pixels at the output frame positions. The second stageuses the motion vector values produced by the first stage to determinethe motion vector value for each pixel in the output frame.

Referring now to FIG. 35, each pixel of the input frame 2 is tested forthe best luminance value match with the previous and following inputframes 1 and 3 of video data, using each of the four motion vectorssupplied. The pixel luminance difference is determined as: ##EQU6##where: P1_(nm) is the luminance value of a frame 1 pixel within a 4×4block of pixels surrounding the pixel whose location is obtained bysubtracting the coordinates of the motion vector being tested from thelocation of the pixel being tested in frame 2

P2_(nm) is the luminance value of a frame 2 pixel within a 4×4 block ofpixels surrounding the pixel being tested

P3_(nm) is the luminance value of a frame 3 pixel within a 4×4 block ofpixels surrounding the pixel whose location is obtained by adding thecoordinates of the motion vector being tested to the location of thepixel being tested in frame 2

The minimum pixel difference then indicates the best luminance match andtherefore the correct motion vector applicable to the pixel beingtested. If the correct motion vector is not available, or there areuncovered or covered areas, referred to in more detail below, then agood match may not occur.

The indication of a poor match is achieved when the average pixeldifference within the block of pixels being used is above a certainthreshold. This threshold is important, because high frequency detailmay produce a poor match even when the correct motion vector is tested.The reason for this poor match is the possibility of a half pixel errorin the motion vector estimate. To determine what threshold shouldindicate a poor match, it is necessary to relate the threshold to thefrequency content of the picture within the block of data whichsurrounds the pixel for which the motion vector is required. To achievethis, an auto-threshold value is determined where the threshold valueequals half the maximum horizontal or vertical pixel luminancedifference about the pixel being tested. To ensure that the thresholdvalue obtained is representative of the whole block of data which iscompared an average value is obtained for the four central pixels of a4×4 block used.

Referring to FIG. 37, which shows a 4×4 block, the required thresholdvalue T is given by:

    T=(T1+T2+T3+T4)/8

where T3, for example, is determined as indicated in FIG. 38 as equal tothe maximum of the four pixel luminance difference values comprising:

the two vertical differences |B2=B3| and |B4-B3|, and

the two horizontal differences |A3-B3| and |C3-B3|

In this way a frame of motion vectors is obtained for input frame 2, andin a similar manner a frame of motion vectors is obtained for inputframe 3 as indicated in FIG. 36.

Apart from scene changes, it is the phenomenon of uncovered/coveredsurfaces that causes a mis-match to occur in the above first stage ofmotion vector selection. If an object, say a car, drives into a tunnel,then the car has become covered, while when it drives out, the car isuncovered. If the part of the car that was uncovered in frames 1 and 2is covered in frame 3 and 4, then the basic vector selection process isnot able to determine the correct vector. Moreover, whilst the car goinginto the tunnel becomes covered, the road and objects behind the car arebeing uncovered. Likewise the car leaving the tunnel is being uncovered,but the road and objects behind the car are being covered. In generaltherefore both covered and uncovered objects will exist at the sametime. The end of a scene will also have a discontinuation of motion thatis similar to an object becoming covered. In an attempt to determine amotion vector even in such circumstances, the luminance value blockmatch is reduced to a two frame match, instead of the three frame matchof FIGS. 35 and 36. The frame that the motion vectors are required for(say frame 2) is block-matched individually to the previous and the nextframe (frame 1 and 3 respectively, in the case of frame 2), using thefour motion vectors supplied. The motion vector which produces the bestmatch is chosen as the motion vector applicable to the pixel beingtested. In this case, however, a flag is set to indicate that only a twoframe match was used.

Particularly with integrating type television cameras, there will besituations where no match occurs. If an object moves over a detailedbackground, then an integrating camera will produce unique portions ofpicture where the loading and trailing edges of the object are mixedwith the detail of the background. In such circumstances, even the twoframe match could produce an average pixel difference above thethreshold vale. In these cases the motion vector value is set to zero,and an error flag is also set.

The second stage of motion vector selection makes use of the two framesof motion vectors, derived by the first stage. One frame of motionvectors (input frame 2) is considered to be the reference frame, and thefollowing frame to this (input frame 3) is also used. The output frameposition then exists somewhere between these two frames of motionvectors. Referring to FIG. 39, for each output pixel position the fourpossible motion vectors associated with the sample block of input frame2, are tested. A line drawn through the output pixel position at theangle of the motion vector being tested will point to a position on boththe input frame 2 and the input frame 3. In the case of odd value motionvectors, for example, 1,3 and 5, a point midway between two input framepixels would be indicated in the case where the output frame isprecisely half way between the input frames 1 and 2. To allow for thisinaccuracy, and also to reduce the sensitivity to individual pixels, a3×3 block of motion vectors is acquired for each frame, centered on theclosest pixel position. In effect a block-match is then performedbetween each of the two 3×3 blocks of motion vectors and a blockcontaining the motion vector being tested. The motion vector differenceused represents the spatial difference of the two motion vector valuesas given by: ##EQU7## where: x1 and y1 are the Cartesian coordinates ofthe motion vector in one of the blocks

x2 and y2 are the Cartesian coordinates of the motion vector beingtested

An average vector difference per pixels is produced as a result of theblock match.

A motion vector match is first produced as above using only motionvector values which are calculated using three input frames; that is,input frames 1, 2 and 3 for input frame 2 (FIG. 35), and input frames 2,3 and 4 for input frame 3 (FIG. 36), and the result is scaledaccordingly. Preferably there are at least four usable motion vectors inthe block of nine. When both the motion vector block of frame 2 andframe 3 can be used, the motion vector difference values are made up ofhalf the motion vector difference value from frame 2 plus half themotion vector difference value from frame 3. Whichever motion vectorproduces the minimum motion vector difference value using the abovetechnique is considered to be the motion vector applicable to the outputpixel being tested. If the motion vector difference value produced bythe three frame match input motion vector (FIG. 35 and 36) is greaterthan unity, then a covered or uncovered surface has been detected, andthe same process is repeated, but this time ignoring the error flags.That is, the motion vector values which were calculated using two inputframes are used. Theoretically this is only necessary foruncovered/covered surfaces, although in fact improvements can beobtained to the picture in more general areas.

If after both of the above tests have been performed, the minimum motionvector match is greater than two, the motion vector value is set tozero, and an error flag is set for use by the motion vector postprocessor 7 (FIG. 1).

Following motion vector selection, there will almost certainly be in anyreal picture situation, some remaining spurious motion vectorsassociated with certain pixels. FIGS. 40 to 45 show what are taken to bespurious motion vectors, and in each of these figures the trianglesrepresent pixels having associated therewith the same motion vectors,whilst the stars represent pixels having associated therewith motionvectors different those associated with the surrounding pixels, and thecircle indicates the motion vector under test.

FIG. 40 shows a point singularity where a single pixel has a motionvector different from those of all the surrounding pixels.

FIG. 41 shows a horizontal motion vector impulse, where threehorizontally aligned pixels have a motion vector different from those ofthe surrounding pixels.

FIG. 42 shows a vertical motion vector impulse where three verticallyaligned pixels have a motion vector different from those of thesurrounding pixels.

FIG. 43 shows a diagonal motion vector impulse where three diagonallyaligned pixels have a motion vector different from those of all thesurrounding pixels.

FIG. 44 shows a horizontal plus vertical motion vector impulse, wherefive pixels disposed in an upright cross have a motion vector differentfrom those of all the surrounding pixels.

FIG. 45 shows a two-diagonal motion vector impulse where five pixelsarranged in a diagonal cross have a motion vector different from thoseof all the surrounding pixels.

It is assumed that pixel motion vectors which fall into any of the abovesix categories do not actually belong to a real picture, and are adirect result of an incorrect motion vector selection. If such motionvectors were used during the interpolation process, then they would belikely to cause dots on the final output picture, and it is thereforepreferable that such motion vectors be identified and eliminated. Thisis done using an algorithm which will detect and flag all of the abovemotion vector groupings.

The algorithm uses a two-pass process, with each pass being identical.The need for two passes will become apparent. FIG. 46, to whichreference is made, shows an array of pixels, all those marked with atriangle having the same motion vector associated therewith. The blockof nine pixels in the centre has motion vectors designated vector 1 tovector 9 associated therewith, which motion vectors may or may not bethe same. Vector 5 is the motion vector under test.

In the first pass, vector 5 is checked to determine whether it is thesame as, or within a predetermined tolerance of:

firstly

vector 1 or vector 3 or vector 7 or vector 9

and secondly

vector 2 or vector 4 or vector 6 or vector 8

This checks to see if vector 5 is the same as at least one of itshorizontal or vertical neighbors and the same as at least one of itsdiagonal neighbours. If this is not the case, then a flag to set toindicate that pixel 5 is bad.

The first pass will flag as bad those motion vectors relating to pointsingularities, horizontal motion vector impulses, vertical motion vectorimpulses, diagonal motion vector impulses and two diagonal motion vectorimpulses (FIGS. 40 to 43 and 45), but not the motion vectorscorresponding to horizontal plus vertical motion vector impulses (FIG.44) for which pass 2 is required. The second pass checks for exactly thesame conditions as in the first pass, but in this case motion vectorswhich have already been flagged as bad are not included in thecalculation. Thus, referring to FIG. 44, after the first pass only thecentre motion vector is flagged as bad, but after the second pass allfive of the motion vectors disposed in the upright cross are flagged asbad.

Having identified the bad motion vectors, it is then necessary to repairthem, this also being effected by the motion vector post processor 7(FIG. 1). Although various methods such as interpolation or majorityreplacement can be used, it has been found that in practice simplereplacement gives good results. This is effected as follows (and itshould be noted that the `equals` signs mean not only exactly equal to,but also being within a predetermined tolerance of):

If vector 5 is flagged as bad then it is replaced with:

vector 4 if (vector 4 equals vector 6)

else with vector 2 if (vector 2 equals vector 8)

else with vector 1 if (vector 1 equals vector 9)

else with vector 3 if (vector 3 equals vector 7)

else do nothing

Referring again to FIG. 1, the finally selected motion vector for eachpixel is supplied by the motion vector post processor 7 to theinterpolator 8, together with the progressive scan converted frames at60 frames per second from the progressive scan converter 2. Theinterpolator 8 is of relatively simple form using only two progressivescan converted frames, as indicated in FIG. 47. Using the temporalposition of the output frame relative to successive input frames, frame1 and frame 2, and the motion vector for the pixel in the output frame,the interpolator 8 determines in known manner which part of the firstframe should be combined with which part of the second frame and withwhat weighting to produce the correct output pixel value. In otherwords, the interpolator 8 adaptively interpolates along the direction ofmovement in dependence on the motion vectors to produce motioncompensated progressive scan frames corresponding to 24 frames persecond. Although the motion vectors have been derived using onlyluminance values of the pixels, the same motion vectors are used forderiving the required output pixel chrominance values. An 8×8 array ofpixels are used from each frame to produce the required output. Thus theinterpolator 8 is a two-dimensional, vertical/horizontal, interpolatorand the coefficients used for the interpolator 8 may be derived usingthe Remez exchange algorithm which can be found fully explained in`Theory and application of digital signal processing`, Lawrence RRabiner, Bernard Gold. Prentice-Hall Inc., pages 136 to 140 and 227.

FIG. 47 shows diagrammatically the interpolation performed by theinterpolator 8 (FIG. 1) for three different cases. The first case, shownon the left, is where there are no uncovered or covered surfaces, thesecond case, shown in the centre, is where there is a covered surface,and the third case, shown on the right, is where there is an uncoveredsurface. In the case of a covered surface, the interpolation uses onlyframe 1, whilst in the case of an uncovered surface, the interpolationuses only frame 2.

Provision can be made in the interpolator 8 to default to non-motioncompensated interpolation, in which case the temporally nearestprogressive scan converted frame is used.

Although illustrative embodiments of the invention have been describedin detail herein with reference to the accompanying drawings, it is tobe understood that the invention is not limited to those preciseembodiments, and that various changes and modifications can be effectedtherein by one skilled in the art without departing from the scope andspirit of the invention as defined by the appended claims.

We claim:
 1. A motion compensated video standards converter forconverting a video signal conforming with a first video standard to aconverted video signal conforming with a second video standard, saidvideo signal being provided as a sequence of intervals selected fromfields and frames, each of said intervals being arranged in a pluralityof blocks each representing a portion of an image represented by saidvideo signal and comprising a plurality of pixels, comprising:comparingmeans for comparing each of a plurality of blocks in a first interval ofsaid video signal with a plurality of blocks in an adjacent interval ofthe video signal for deriving a respective plurality of motion vectorsfor at least some of said plurality of blocks in said first intervalrepresenting motion of the image portion represented by said at leastsome of said plurality of blocks; means for selecting further motionvectors for assignment to those of said plurality of blocks in saidfirst interval for which less than a predetermined plurality of motionvectors are derived as a result of said comparison, by selecting saidfurther motion vectors from a group of non-stationary motion vectorsderived for said first interval including those motion vectors whichoccur most frequently and excluding those motion vectors which fallwithin a predetermined window of pixel motion of a more frequentlyoccurring motion vector; and interpolator means for producing saidconverted video signal, said interpolator means being controlled atleast in dependence on said respective plurality of motion vectors andsaid further motion vectors.
 2. A motion compensated video standardsconverter according to claim 1 wherein said means for selecting isoperative to select said further motion vectors from said group ofnon-stationary motion vectors excluding motion vectors falling withinone pixel motion of a more frequently occurring motion vector.
 3. Amotion compensated video standards converter according to claim 1wherein said comparing means and said means for selecting are operativeto assign three non-zero motion vectors to each of said blocks.
 4. Amotion compensated video standards converter according to claim 1wherein said comparing means and said means for selecting are operativeto assign four non-zero motion vectors to each of said blocks.
 5. Amotion compensated video standards converter according to claim 1wherein said comparing means is operative to compare sums of respectiveluminance level differences of corresponding pixels in respective blocksbeing compared.
 6. A method of deriving motion vectors representingmotion between successive intervals of a video signal, said intervalsbeing selected from fields and frames, each of said intervals beingarranged in a plurality of blocks each representing a portion of animage represented by said video signal and comprising a plurality ofpixels, the method including the steps of:comparing each of a pluralityof blocks in a first interval of said video signal with a plurality ofblocks in an adjacent interval of the video signal for deriving arespective plurality of motion vectors for at least some of saidplurality of blocks in said first interval representing motion of theimage portion represented by said at least some of said plurality ofblocks; and selecting further motion vectors for assignment to those ofsaid plurality of blocks in said first interval for which less than apredetermined plurality of motion vectors are derived as a result ofsaid comparison, by selecting said further motion vectors from a groupof non-stationary motion vectors derived for said first intervalincluding those motion vectors which occur most frequently and excludingthose motion vectors which fall within a predetermined window of pixelmotion of a more frequently occurring motion vector.
 7. A methodaccording to claim 6 wherein the step of selecting further motionvectors comprises selecting said further motion vectors from said groupof non-stationary motion vectors excluding motion vectors falling withinone pixel motion of a more frequently occurring motion vector.
 8. Amethod according to claim 6 wherein the steps of comparing each of aplurality of blocks and selecting further motion vectors further includeassigning three non-zero motion vectors to each of said blocks.
 9. Amethod according to claim 6 wherein the steps of comparing each of aplurality of blocks and selecting further motion vectors further includeassigning four non-zero motion vectors to each of said blocks.
 10. Amethod according to claim 6 wherein said step of comparing each of aplurality of blocks includes comparing sums of respective luminancelevel differences of corresponding pixels in respective blocks beingcompared.